The proposed deep saliency network
is implemented with the Caffe library [1]}.
While training, the weights of the filters in the five
convolution blocks of the encoder network are initialized
from the VGG16, which is trained on the ImageNet [2]} database
for the task of classification. The weights of the remaining layers are
randomly initialized from a Gaussian distribution with zero mean and standard deviation of 0.01.
We train the networks on the 10,000 images from the
SALICON [3]} training set where eye fixation annotations
are simulated through mouse movements of users on blurred images.
The authors of [3]} show that the mouse-contingent
saliency annotations strongly correlate with actual eye-tracker annotations.
A mini-batch of 16 images is used in each iteration.
The learning rate is initialized as \(1\times 10^{-4}\)  and scaled down by a factor of 0.1 after 2,000 iterations.
The network was validated against the SALICON validation set (5,000 images) after every 100 iterations to monitor
convergence and overfitting.
The network parameters are learned by back-propagating the loss function in (REF )
using Stochastic Gradient Descent (SGD) with momentum.
During testing, given a query image, we obtain
final saliency prediction from the last multi-scale attention fusion layer. Our model achieves
processing speed as little as 0.1 seconds on our PC with a TITANX GPU and 32G RAM.
